scholarly journals Investigation of Model Human Processor by EEG Measurement

2017 ◽  
Vol 112 ◽  
pp. 2040-2047 ◽  
Author(s):  
Toshitaka Higashino ◽  
Yudai Asano ◽  
Masato Soga
Keyword(s):  
Model Human Processor

scholarly journals Development of an Adaptive Workload Management System using the Queueing Network-Model Human Processor

2007 ◽  
Vol 51 (24) ◽  
pp. 1540-1544
Author(s):  
Changxu (Sean) Wu ◽  
Omer Tsimhoni ◽  
Yili Liu
Keyword(s):  
Network Model ◽  
Management System ◽  
Human Performance ◽  
Experimental Studies ◽  
Queueing Network ◽  
Workload Management ◽  
Driver Performance ◽  
Vehicle Systems ◽  
Driver Workload ◽  
Model Human Processor

Drivers overloaded with information from in-vehicle systems significantly increase the chance of vehicle collisions. Developing adaptive workload management systems (AWMS) to dynamically control the rate of messages from these in-vehicle systems is one of the solutions to this problem. However, existing AWMS do not use driver models to estimate workload, and only suppress or redirect messages without changing the rate of messages from the in-vehicle systems. In this work, we propose a prototype of a new adaptive workload management system, the Queuing Network-Model Human Processor (QN-MHP) AWMS, which includes a model of driver workload based on the queueing network theory of human performance that estimates driver workload in different driving situations and a message controller that dynamically controls the rate of messages presented to drivers. Corresponding experimental studies were conducted to validate the potential effectiveness of this system in reducing driver workload and improving driver performance.

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